Comorbidity‐driven multi‐modal subtype analysis in mild cognitive impairment of Alzheimer's disease

Abstract

Background Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi‐modality data‐driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well‐known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD‐related comorbidities along with other AD‐relevant features and biomarkers. Methods A total of 325 MCI subjects with 32 AD‐relevant comorbidities and features were considered. Mixed‐data clustering is applied to discover and compare MCI subtypes with and without including AD‐related comorbidities. Finally, the relevance of each comorbidity‐driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. Results We identified four (five) MCI subtypes: poor‐, average‐, good‐, and best‐AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity‐driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity‐driven MCI subtypes. Among the four comorbidity‐driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test‐related and plasma features, and by the proportion of comorbidities. Conclusions Our study indicates that AD comorbidities should be considered along with other diverse AD‐relevant characteristics to better understand MCI heterogeneity.

CSEPC: Deep Learning Framework for Small Sample Multimodal Medical Image Data in Alzheimer’s Disease Prediction

“…Recent research has emphasized the importance of coupled features, which help link brain structure and function, as a new metric, thus enabling a deeper investigation of brain structural differences while enhancing the understanding of AD-related pathological changes [27,28]. Katabathula et al [29] have proposed that to fully understand the speci city of MCI and explore the pathological differences among various MCI subcategories, the integration of multimodal features is crucial. Our proposed CSEPC framework leverages cosine coupling computation to extract complementary features between modalities from multimodal MRI data.…”

Trajectories of health conditions and their associations with the risk of cognitive impairment among older adults: insights from a national prospective cohort study

“…The remitting and relapsing nature of sleep disorders and depressive symptoms necessitates an investigation into their course in relation to the risk of cognitive impairment. Additionally, sleep disorders, depressive symptoms, and functional limitations were associated with some chronic diseases, such as cardiovascular diseases, which were also related to cognitive impairment [ 10 , 11 ]. Consequently, extended observation over a longer period could uncover more insightful associations that a single assessment might overlook.…”

Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's

“… 9 , 18 , 19 We also present a novel classification method to contrast against existing methods to determine the optimal prediction performance using lower‐cost features. We hypothesize that utilizing a combination of classification and clustering methods, which have been demonstrated to be useful in identifying meaningful clinical subgroups in AD, 14 , 20 will allow us to stratify subjects into subgroups that may be of interest for clinical intervention.…”